Data Science Certified Training

01. Course Overview

Accelerate your career with the exclusive Data Scientist Master’s Program co-developed with IBM. Experience world-class training by an industry leader on the most in-demand Data Science and Machine learning skills. Gain hands-on exposure to key technologies including R, SAS, Python, Tableau, Hadoop and Spark. Become an expert Data Scientist today.

02. Why Data Science?

A Data scientist is the top ranking professional in any analytics organization. Glassdoor ranks Data Scientists first in the 25 Best Jobs for 2019. In today’s market, Data Scientists are scarce and in demand. As a Data Scientist, you are required to understand the business problem, design a data analysis strategy, collect and format the required data, apply algorithms or techniques using the correct tools, and make recommendations backed by data.

03. What are The Objectives?

Data Scientist is one of the hottest professions. IBM predicts the demand for Data Scientists will rise by 28% by 2020. Simplilearn's Data Scientist Master’s Program co-developed with IBM encourages you to master skills including statistics, hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS Macros, recommendation engine, supervised ,and unsupervised learning and more.

This Data Scientist Master’s Program covers extensive Data Science training, combining online instructor-led classes and self-paced learning co-developed with IBM. The program concludes with a capstone project designed to reinforce the learning by building a real industry product encompassing all the key aspects learned throughout the program. The skills focused on in this program will help prepare you for the role of a Data Scientist.

04. What are You Learn From This Course?
  • Gain an in-depth understanding of data structure and data manipulation

  • Understand and use linear and non-linear regression models and classification techniques for data analysis

  • Obtain an in-depth understanding of supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline

  • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave

  • Gain expertise in mathematical computing using the NumPy and Scikit-Learn packages

  • Understand the different components of the Hadoop ecosystem

  • Learn to work with HBase, its architecture, and data storage, learning the difference between HBase and RDBMS, and use Hive and Impala for partitioning

  • Understand MapReduce and its characteristics, plus learn how to ingest data using Sqoop and Flume

  • Master the concepts of recommendation engine and time series modeling and gain practical mastery over principles, algorithms, and applications of machine learning

  • Learn to analyze data using Tableau and become proficient in building interactive dashboards

05. First Course Contents: Syllabus for Students

Lesson 1: Introduction

  • Introduction

Lesson 2: Data Analytics Overview

  • Data Analytics Process

  • Knowledge Check

  • Exploratory Data Analysis(EDA)

  • EDA-Quantitative Technique

  • EDA - Graphical Technique

  • Data Analytics Conclusion or Predictions

  • Data Analytics Communication

  • Data Types for Plotting

  • Data Types and Plotting

Lesson 3: Statistical and Business Applications

  • Introduction to Statistics

  • Statistical and Non-statistical Analysis

  • Major Categories of Statistics

  • Statistical Analysis Considerations

  • Population and Sample

  • Statistical Analysis Process

  • Data Distribution

  • Dispersion

  • Knowledge Check

  • Histogram

  • Knowledge Check

  • Testing

  • Knowledge Check

  • Correlation and Inferential Statistics

Lesson 4: Python Environment Setup and Essentials

  • Anaconda

  • Installation of Anaconda Python Distribution (contd.)

  • Data Types with Python

  • Basic Operators and Functions

Lesson 5: Mathematical Computing with Python

  • Introduction to Numpy

  • Activity-Sequence it Right

  • Demo 01-Creating and Printing a ndarray

  • Knowledge Check

  • Class and Attributes of ndarray

  • Basic Operations

  • Activity-Slice It

  • Copy and Views

  • Mathematical Functions of Numpy

Lesson 6: Scientific Computing with Python

  • Introduction to SciPy

  • SciPy Sub Package - Integration and Optimization

  • Knowledge Check

  • SciPy subpackage

  • Demo - Calculate Eigenvalues and Eigenvector

  • Knowledge Check

  • SciPy Sub Package - Statistics, Weave, and IO

Lesson 7: Data Manipulations with Pandas

  • Introduction to Pandas

  • Knowledge Check

  • Understanding DataFrame

  • View and Select Data Demo

  • Missing Values

  • Data Operations

  • Knowledge Check

  • File Read and Write Support

  • Knowledge Check-Sequence it Right

  • Pandas SQL Operation

Lesson 9: Machin Learning with Scikit-Learn

  • Machine Learning Approach

  • Steps 1 and 20

  • Steps 3 and 4

  • How it Works

  • Steps 5 and 6

  • Supervised Learning Model Considerations

  • Knowledge Check

  • Scikit-Learn

  • Knowledge Check

  • Supervised Learning Models - Linear Regression

  • Supervised Learning Models - Logistic Regression

  • Unsupervised Learning Models

  • Pipeline

  • Model Persistence and Evaluation

Lesson 10: Data Visualization in Python using Matplotlib

  • Introduction to Data Visualization

  • Knowledge Check

  • Line Properties

  • (x,y) Plot and Subplots

  • Knowledge Check

  • Types of Plots